1,794 research outputs found
GCN-Based Linkage Prediction for Face Clustering on Imbalanced Datasets: An Empirical Study
In recent years, benefiting from the expressive power of Graph Convolutional
Networks (GCNs), significant breakthroughs have been made in face clustering.
However, rare attention has been paid to GCN-based clustering on imbalanced
data. Although imbalance problem has been extensively studied, the impact of
imbalanced data on GCN-based linkage prediction task is quite different, which
would cause problems in two aspects: imbalanced linkage labels and biased graph
representations. The problem of imbalanced linkage labels is similar to that in
image classification task, but the latter is a particular problem in GCN-based
clustering via linkage prediction. Significantly biased graph representations
in training can cause catastrophic overfitting of a GCN model. To tackle these
problems, we evaluate the feasibility of those existing methods for imbalanced
image classification problem on graphs with extensive experiments, and present
a new method to alleviate the imbalanced labels and also augment graph
representations using a Reverse-Imbalance Weighted Sampling (RIWS) strategy,
followed with insightful analyses and discussions. The code and a series of
imbalanced benchmark datasets synthesized from MS-Celeb-1M and DeepFashion are
available on https://github.com/espectre/GCNs_on_imbalanced_datasets.Comment: 7 page
Adaptive Face Recognition Using Adversarial Information Network
In many real-world applications, face recognition models often degenerate
when training data (referred to as source domain) are different from testing
data (referred to as target domain). To alleviate this mismatch caused by some
factors like pose and skin tone, the utilization of pseudo-labels generated by
clustering algorithms is an effective way in unsupervised domain adaptation.
However, they always miss some hard positive samples. Supervision on
pseudo-labeled samples attracts them towards their prototypes and would cause
an intra-domain gap between pseudo-labeled samples and the remaining unlabeled
samples within target domain, which results in the lack of discrimination in
face recognition. In this paper, considering the particularity of face
recognition, we propose a novel adversarial information network (AIN) to
address it. First, a novel adversarial mutual information (MI) loss is proposed
to alternately minimize MI with respect to the target classifier and maximize
MI with respect to the feature extractor. By this min-max manner, the positions
of target prototypes are adaptively modified which makes unlabeled images
clustered more easily such that intra-domain gap can be mitigated. Second, to
assist adversarial MI loss, we utilize a graph convolution network to predict
linkage likelihoods between target data and generate pseudo-labels. It
leverages valuable information in the context of nodes and can achieve more
reliable results. The proposed method is evaluated under two scenarios, i.e.,
domain adaptation across poses and image conditions, and domain adaptation
across faces with different skin tones. Extensive experiments show that AIN
successfully improves cross-domain generalization and offers a new
state-of-the-art on RFW dataset.Comment: Accepted by TI
Image Clustering using Restricted Boltzman Machine
In various verification systems, Restricted Boltzmann Machines (RBMs) have
demonstrated their efficacy in both front-end and back-end processes. In this
work, we propose the use of RBMs to the image clustering tasks. RBMs are
trained to convert images into image embeddings. We employ the conventional
bottom-up Agglomerative Hierarchical Clustering (AHC) technique. To address the
challenge of limited test face image data, we introduce Agglomerative
Hierarchical Clustering based Method for Image Clustering using Restricted
Boltzmann Machine (AHC-RBM) with two major steps. Initially, a universal RBM
model is trained using all available training dataset. Subsequently, we train
an adapted RBM model using the data from each test image. Finally, RBM vectors
which is the embedding vector is generated by concatenating the
visible-to-hidden weight matrices of these adapted models, and the bias
vectors. These vectors effectively preserve class-specific information and are
utilized in image clustering tasks. Our experimental results, conducted on two
benchmark image datasets (MS-Celeb-1M and DeepFashion), demonstrate that our
proposed approach surpasses well-known clustering algorithms such as k-means,
spectral clustering, and approximate Rank-order
A Survey of Deep Graph Clustering: Taxonomy, Challenge, and Application
Graph clustering, which aims to divide the nodes in the graph into several
distinct clusters, is a fundamental and challenging task. In recent years, deep
graph clustering methods have been increasingly proposed and achieved promising
performance. However, the corresponding survey paper is scarce and it is
imminent to make a summary in this field. From this motivation, this paper
makes the first comprehensive survey of deep graph clustering. Firstly, the
detailed definition of deep graph clustering and the important baseline methods
are introduced. Besides, the taxonomy of deep graph clustering methods is
proposed based on four different criteria including graph type, network
architecture, learning paradigm, and clustering method. In addition, through
the careful analysis of the existing works, the challenges and opportunities
from five perspectives are summarized. At last, the applications of deep graph
clustering in four domains are presented. It is worth mentioning that a
collection of state-of-the-art deep graph clustering methods including papers,
codes, and datasets is available on GitHub. We hope this work will serve as a
quick guide and help researchers to overcome challenges in this vibrant field.Comment: 13 pages, 13 figure
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